Narrow-band and derivative-based vegetation indices for hyperspectral data

Hyperspectral remote sensing imagery was collected over a soybean field in central Illinois in mid-June 2001 before canopy closure. Estimates of percent vegetation cover were generated through the processing of RGB (red, green, blue) digital images collected on the ground with an automated crop mapping system. A comparative study was completed to test the ability of broad-band, narrow-band, and derivative-based vegetation indices to predict percent soybean cover at levels less than 70%. Though remote sensing imagery is commonly analysed using reference data collected at random points over a scene of interest, the analysis of the hyperspectral imagery in this research was performed on a pixel-by-pixel basis over the field area covered by the automated crop mapping system. Narrow-band and derivative-based indices utilizing the finer spectral detail of hyperspectral data performed better than the older broad-band indices developed for use with multispectral data. Specifically, second-derivative indices measuring the curvature in the green region (514-556 nm), longer wavelength red region (640-694 nm), and short wavelength NIR (712-778 nm) performed well. Narrow-band indices, based on the standard ratio index equations, which used values from the blue (472-490 nm) and green (514-550 nm) regions, also performed well for many of the datasets. The performance of all indices was shown to suffer over areas of brighter soil background, and the use of ratio-based narrow-band indices that did not incorporate NIR reflectance values performed best in this cas

Meal patterning and the onset of spontaneous labor

Background: There is a lack of consensus in the literature about the association between meal patterning during pregnancy and birth outcomes. This study examined whether maternal meal patterning in the week before birth was associated with an increased likelihood of imminent spontaneous labor. Methods: Data came from 607 participants in the third phase of the Pregnancy, Infection, and Nutrition Study (PIN3). Data were collected through an interviewer-administered questionnaire after birth, before hospital discharge. Questions included the typical number of meals and snacks consumed daily, during both the week before labor onset and the 24-hour period before labor onset. A self-matched, case-crossover study design examined the association between skipping one or more meals and the likelihood of spontaneous labor onset within the subsequent 24 hours. Results: Among women who experienced spontaneous labor, 87.0% reported routinely eating three daily meals (breakfast, lunch, and dinner) during the week before their labor began, but only 71.2% reported eating three meals during the 24-hour period before their labor began. Compared with the week before their labor, the odds of imminent spontaneous labor were 5.43 times as high (95% CI: 3.41-8.65) within 24 hours of skipping 1 or more meals. The association between skipping 1 or more meals and the onset of spontaneous labor remained elevated for both pregnant individuals who birthed early (37-<39 weeks) and full-term (≥39 weeks). Conclusions: Skipping meals later in pregnancy was associated with an increased likelihood of imminent spontaneous labor, though we are unable to rule out reverse causality

Balancing food production within the planetary water boundary

Freshwater use is recognized as one of the nine planetary boundaries. However, water scarcity is a local or regional phenomenon, meaning that the global boundary must be spatially downscaled to reflect differences in water availability. In China, as in most countries, irrigation is the major freshwater user, closely linking food security to the freshwater boundary. To provide evidence supporting environmentally sustainable water use in China's food production, this study explores how a grain production shift affects the national water-scarcity footprint (WSF) and the potential to reach sustainable water use limits while maintaining the current grain production level. We found that the historical breadbasket shift towards water-scarce northern regions has increased the WSF by 40% from 1980 to 2015. To operate within the boundary, national irrigation needs to be reduced by 18% in hotspot regions, with implications of a 21% loss of grain production. However, this loss can be reduced to around 8% by closing yield gaps in water-rich regions. It demonstrates the high potential of integrating crop redistribution and closing yield gaps to achieve grain production goals within freshwater boundaries. This Chinese case study can be representative of the challenges faced by many of the world's countries, where pressures on land and water resources are high and a sustainable means of increasing food supply must be found. (C) 2020 The Author(s). Published by Elsevier Ltd.Industrial Ecolog

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles

Advancing the application of a model-independent open-source geospatial tool for national-scale spatiotemporal simulations

Industrial Ecolog

Agronomic Outcomes of Precision Irrigation Management Technologies with Varying Complexity

Diverse technologies, methodologies, and data sources have been proposed to inform precision irrigation management decisions, and the technological complexity of different solutions is highly variable. Additional field studies are needed to identify solutions that achieve intended agronomic outcomes in simple and cost-effective ways. The objective of this study was to compare cotton yield and water productivity outcomes resulting from different solutions for scheduling and conducting precision irrigation management. A cotton field study was conducted at Maricopa, Arizona, in 2019 and 2020 that evaluated the outcomes of four management solutions with varying technological complexity: (1) a stand-alone evapotranspiration-based soil water balance model with field-average soil parameters (MDL), (2) using site-specific soil data to spatialize the modeling framework (SOL), (3) driving the model with spatial crop coefficients estimated from an unoccupied aircraft system (UAS), and (4) using commercial variable-rate irrigation technology for site-specific irrigation applications (VRI). Soil water content data and thermal UAS data were also collected but used only in post hoc data analysis. Applied irrigation, cotton fiber yield, and water productivity were statistically identical for MDL and SOL. As compared to MDL, the UAS crop coefficient approach significantly reduced applied irrigation by 7% and 14% but also reduced yield by 5% and 26% in 2019 and 2020, respectively (p = 0.05). In 2019 only, the VRI approach maintained yield while significantly reducing applied irrigation by 8% compared to MDL, and water productivity was significantly increased from 0.200 to 0.211 kg m-3 when one outlier datum was removed (p = 0.05). Post hoc data analysis showed that crop water stress information, particularly from UAS thermal imaging data, would likely benefit the irrigation scheduling protocol. Efforts to develop integrated sensing and modeling tools that can guide precision irrigation management to achieve intended agronomic outcomes should be prioritized and will be relevant whether irrigation applications are site-specific or uniform. © 2022 The authors.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems

Small unoccupied aircraft systems (sUAS) are becoming popular for mapping applications in agriculture, and photogrammetry software is available for developing orthorectified imagery and three-dimensional surface models. Ground control points (GCPs), which are objects or locations with known geographic coordinates, may be required for accurate image georeferencing. However, few studies have compared global position equipment among sUAS or investigated the effects of GCP number or arrangement on georeferencing accuracy. The objectives of this study were to evaluate numbers and configurations of GCPs for georeferencing sUAS-acquired images and determine the GCP requirements for sUAS with and without real-time kinematic (RTK) global positioning equipment. The effects of varying numbers and configurations of GCPs were investigated on both a 0.40-ha area the size of a typical plant breeding trial and a 64.7-ha area (i.e., a U.S. quarter section) the size of a typical agricultural production field. Results demonstrated that four GCPs placed at the corners of the breeding-scale field resulted in two-dimensional (2D) error of ±3 cm in the absence of RTK, with minimal improvements when including more GCPs. The orthomosaics from the RTK-equipped sUAS demonstrated improved 2D accuracy even without the use of GCPs, with a maximum mean error of 0.08 m. Four GCPs were found to be sufficient to reduce altitudinal (Z) error, with maximum mean error of only 0.05 and 1.98 m for the RTK and non-RTK flights, respectively, for the production-scale field. Thus, using four GCPs, RTK-equipped sUAS, or a combination will result in improved georeferencing for photogrammetry products. © 2021 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of AmericaOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Using ESAP software for predicting the spatial distributions of NDVI and transpiration of cotton

Observations of the normalized difference vegetation index (NDVI) from aerial imagery can be used to infer the spatial variability of basal crop coefficients (Kcb), which in turn provide a means to estimate variable crop water use within irrigated fields. However, monitoring spatial Kcb at sufficient temporal resolution using only aerial acquisitions would likely not be cost-effective for growers. In this study, we evaluated a model-based sampling approach, ESAP (ECe Sampling, Assessment, and Prediction), aimed at reducing the number of seasonal aerial images needed for reliable Kcb monitoring. Aerial imagery of NDVI was acquired over an experimental cotton field having two treatments of irrigation scheduling, three plant density levels, and two N levels. During both 2002 and 2003, ESAP software used input imagery of NDVI on three separate dates to select three ground sampling designs having 6, 12, and 20 sampling locations. On three subsequent dates during both the years, NDVI data obtained at the design locations were then used to predict the spatial distribution of NDVI for the entire field. Regression of predicted versus imagery observed NDVI resulted in r2 values from 0.48 to 0.75 over the six dates, where higher r2 values occurred for predictions made near full cotton cover than those made at partial cover. Prediction results for NDVI were generally similar for all three sample designs. Cumulative transpiration (Tr) for periods from 14 to 28 days was calculated for treatment plots using Kcb values estimated from NDVI. Estimated cumulative Tr using either observed NDVI from imagery or predicted NDVI from ESAP procedures compared favorably with measured cumulative Tr determined from soil water balance measurements for each treatment plot. Except during late season cotton senescence, errors in estimated cumulative Tr were between 3.0% and 7.3% using observed NDVI, whereas they were they were between 3.4% and 8.8% using ESAP-predicted NDVI with the 12 sample design. Thus, employing a few seasonal aerial acquisitions made in conjunction with NDVI measurements at 20 or less ground locations optimally determined using ESAP, could provide a cost-effective method for reliably estimating the spatial distribution of crop water use, thereby improving cotton irrigation scheduling and management.Remote sensing Crop coefficients Irrigation management Crop water use